Difficulties arise in producing a unified view of data that is spread across multiple and/or overlapping systems that nevertheless describe aspects of the same domain of discourse. For example, a sophisticated telecommunications network may have a plethora of data stored in a multiplicity of formats such that a unified data scheme cannot be readily obtained or accessed. Put another way, there exist enormous challenges in enriching data from multiple data sources with implicit facts that can be inferred from the data contained in those sources, managing overlaps in the data sources, and presenting a unified view of the data and inferred facts.
The prior art typically attempts to solve these problems using one of the following methods:                direct integration of external data sources to a central schema representing a unified view of the data; or        integration of the external data sources to an “Enterprise Integration Bus” or “Enterprise Service Bus” and then enabling communications from the software providing the unified view to the external systems through the bus; or        employing semantic federation, whereby the external data sources are mapped to semantic knowledge models which are then bridged using well understood approaches, such as bridging axioms and class and property subsumption.        
For a variety of reasons, all of these solutions are inadequate or inappropriate for many of the most commercially significant problem domains.